On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification
Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning methods require the collection of both traini...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/18/3054 |